Web manufacturing refers to production and/or processing of long, thin sheets of bendable, flexible and/or soft material, in particular paper, cardboard, textile, plastic film, foil, (sheet) metal, and sometimes wire, commonly referred to as web. During production or processing, a web is generally transported over rollers in a moving direction (MD). Alternatively, the web may also be transported on some kind of conveyor belt, which may in particular be a (woven) mesh, e.g. in a so called Fourdrinier process and/or machine.
Between processing stages, webs may be stored and transported as rolls also referred to as coils, packages and doffs. A final result of web manufacturing usually comprises sheets being separated from the web by cutting or otherwise separating in a cross direction (CD) perpendicular to the moving direction. A main reason for work with webs instead of sheets is economics. Webs, being continuous, may generally be produced and/or processed at higher speeds than sheets, without start-stop issues which are inherent to production and/or processing of sheets.
For supervision and/or quality control of web manufacturing processes, web inspection systems are frequently applied which use digital imaging techniques, in particular image capture and image processing, for detecting defects or other anomalies. For web manufacturing of paper or cardboard, holes, spots and dirt particles are examples of strong defects, frequently briefly referred to as defects, whereas wrinkles, streaks and slime spots are examples of weak defects. Correspondingly, for web manufacturing of sheet metal makers, slag inclusions, cracks and scratches are examples of strong defects whereas weak cracks, weak scratches and indentations are examples of weak defects.
Defects give rise to local deviations of various characteristic image quantities, in particular of a pixel intensity level, from average and/or expected values. In the above examples, weak defects cause only a slight change in an intensity level of the digital video signal as compared to a mean variation of the intensity level measured from a faultless product. Strong defects, on the other hand, give generally rise to substantial deviations.
In addition to defect detection, supervision and/or quality control of manufacturing processes, in particular web manufacturing, may include observation, monitoring, surveying, etc. to detect a presence and/or absence, and/or a frequency, number, size, distinctness, visibility etc., of other properties, characteristics, qualities, etc. Such properties, characteristics, or qualities may include wanted and/or unwanted irregularities or unevenness of a product produced by the manufacturing processes, in particular of the web.
In particular in papermaking, formation, which may be thought of as a local non-uniformity of a sheet structure, is one such property or characteristic, and a key quality factor of the paper. Also in some other web products like for example glass fiber there are same kind of formation, i.e. non-uniform fiber clusters are causing flocs, which appear as cloudiness when one looks through the product. Also in some products there are uneven surfaces like for example coated paper with mottling, which means unwanted uneven print density and color variations. Earlier solutions for paper or surface formation floc analysis are based on off line lab measurements, snapshot, narrow band or scanning imaging methods and thus they are not capable of covering the whole web in real-time.
Formation describes how uniformly the fibers and fillers are distributed in the paper sheet. Formation is an important factor because most of the paper properties depend on it. The weakest paper properties define the end quality of paper. Bad formation causes the paper to have more weak and thin or thick areas. These affect properties like opacity and strength etc. Paper formation also affects the coating and printing characteristics of the paper. Formation problems can cause uneven dotting and mottling effect when printed. There is none standard method or unit to describe formation. It can be relative, objective or subjective evaluation.
Properties, characteristics, qualities, etc. related to formation are frequently referred to as formation features, or, in short, features.
The basic assumption behind the use of digital imaging techniques for supervision and/or quality control of web manufacturing processes is that the properties, characteristics, qualities as described above are reflected in images taken of the web or otherwise obtained. By choosing appropriate illumination and imaging setup, the defects or other properties, characteristics, qualities, etc. as described above cause intensity variations in the respective images, which in turn allow to detect their presence or absence.
Light transmission measurement or analysis can, in particular, be used for analyzing paper formation, which is often defined as variation of the mass distribution of a sheet of paper. The paper formation can be seen by holding any sheet up to the light and observing the “look through”. Good formation appears uniform while bad formation has bundles of fibers causing a cloudy look. Good formation normally requires small floc sizes, which improve printability of a paper product. Several paper formation test methods have been introduced during past few decades. Most of them have been based on visible light transmission to obtain an opacity map of the sheet and then determine the histogram of gray levels of the opacity map and calculate some index of non-uniformity. Paper opacity and paper grammage are usually related but may differ depending on the light scattering properties of paper, thus based on earlier research results if more precise local grammage measurement is needed for example beta-radiation based measurement should be applied.
Another approach to describe the uniformity of formation is to analyze both the opacity variations, and the size and shape statistics of the formation flocs and/or of voids. Typically, increased floc size indicates degraded structural paper properties like cloudiness and unevenness. Large flocs can cause for example poor and uneven ink penetration. One advantage of the floc area analysis is that the measurement values are tolerant of changing lighting conditions due to, for example, environmental reasons like dirt in the imaging system, illumination non-idealities or camera optics non-idealities.
Correspondingly, optical reflection measurement or analysis, in particular, may be used for surface formation blob analysis. Unprinted paper or paperboard surface non-uniformity can be caused by, for example, surface topography variations, surface reflectance variations, and/or coating variations. And in printed products the printing quality variations can be seen as mottling, which can be defined as undesired unevenness in observed print density. All of the above mentioned reflection measurement based imaging results can also be analyzed based on methodologies corresponding to the ones used with transmission measurement.
The most traditional method of optical paper formation analysis is visual (manual) “look through” test by holding a paper against a light source. Two paper formation image examples are presented in FIG. 1. These images are based on visible light transmission measurement. Differences of the formation may clearly be seen. The left paper sample has larger flocs and more “cloudy” appearance. If one inspects the intensity histograms shown below the images in FIG. 1, one notices that the intensity histogram does not reveal the floc “cloudiness” differences of paper. This disadvantage is present in many traditional formation analysis methods.
There are also many formation analysis methods, which utilize spatial information to analyze formation. One example is the so called Kajaani index, which is based on comparison of several different size average windows, as, e.g., described in U.S. Pat. No. 5,113,454 A. The analysis based on this principle is certainly giving some valuable information about formation but the resolution and average window shapes are not optimal for real floc or blob shape analysis.
PaperPerFect method (described, e.g. in Bernié, J. P. and Karlsson, H., “Formation Guide—Measuring optical formation and applications in the paper industry”, A Handbook, Second edition, Lorentzen & Wettre, 2010; or in 4. Bernié, J. P., “Measuring Formation of Paper—PaperPerFect Method—”, A Handbook, Lorentzen & Wettre, 2004) and several other methods, as a further example, utilize frequency domain spectral analysis based on Fast Fourier Transform (FFT). FFT can be used to analyze periodic signals and thus measure the signal wavelengths. It is very suitable to be used for spatially stationary periodic signals like web wire or felt marking. In the case of measurement and analysis of optical formation, the FFT based analysis result does not include the floc or blob spatial location in the measurement area and it is thus possible to get the same spectral analysis results by different spatial domain images. Additionally, with FFT based analysis it is not possible to reveal and visualize an individual floc shape and analyze more precisely floc or blob morphometric properties. Also if periodic features are present, and thus some periodic signals appear, the optical formation analysis result can be dominated by the periodic signal and its harmonic components responses and the real floc size responses can be missed.
There are also some available approaches with combination of spectral analysis and spatial image analysis. Zellcheming technical sub-committee “Online sensor technology” researched this area and published a proposal for standardizing online paper formation measurement, as described in Keller, G., “A Proposal for Standardizing online paper formation measurement”, PTS News 02/2009. In this case the spatial filtering is utilizing only CD and MD direction line profiles of the original image for analyzing floc sizes and orientation based on the floc size measurements in the directions of the principal axes (MD and CD). This method does not propose tools for 2D floc or blob size categorized shape analysis or formation quality change detection.
Saarela. A., “An online formation analysis function is the latest add-on to ABB's web imaging system—Optimize product quality and yield”, Pulp & Paper International (PPI), 2009, introduces a formation analysis method, which utilizes fine scale spatial structure of optical formation. The structure and spatial properties of formation are not dependent on absolute intensity values, and thus the method is not sensitive to illumination changes. This method is not giving any information about formation floc or surface formation blob size categorized power and thus some valuable information is missing.
MILLAN ET AL., “Flaw detection and segmentation in textile inspection”—Optical and Digital Image Processing, 1 Jan. 2008, discusses a method to automatically segment local defects in a woven fabric that does not require any additional defect-free reference for comparison. Firstly, the structural features of the repetition pattern of the minimal weave repeat are extracted from the Fourier spectrum of the sample under inspection. The corresponding peaks are automatically identified and removed from the fabric frequency spectrum. The a set of multi-scale oriented bandpass filters are defined and adapted to the specific structure of the sample, that operate in the Fourier domain. Using the set of filters, local defects can be extracted. Finally, the filtered images obtained at different scales are inverse Fourier transformed, binarized and merged to obtain an output image where flaws are segmented from the fabric background. The method can be applied to fabrics of uniform color as well as to fabrics woven with threads of different colors.
One of the most significant disadvantages of the currently known formation analysis methods and systems is the lack of measurement coverage of the product. Most of the available optical formation analysis systems are covering only small portion of the product using offline lab imaging, web scanning, narrow band measurement area or snapshot imaging. In these cases the papermaker can miss some important process behaviors, which could be revealed by real-time full web optical formation analysis.